DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition (2203.12273v4)

Abstract: Unconstrained handwritten text recognition is a challenging computer vision task. It is traditionally handled by a two-step approach, combining line segmentation followed by text line recognition. For the first time, we propose an end-to-end segmentation-free architecture for the task of handwritten document recognition: the Document Attention Network. In addition to text recognition, the model is trained to label text parts using begin and end tags in an XML-like fashion. This model is made up of an FCN encoder for feature extraction and a stack of transformer decoder layers for a recurrent token-by-token prediction process. It takes whole text documents as input and sequentially outputs characters, as well as logical layout tokens. Contrary to the existing segmentation-based approaches, the model is trained without using any segmentation label. We achieve competitive results on the READ 2016 dataset at page level, as well as double-page level with a CER of 3.43% and 3.70%, respectively. We also provide results for the RIMES 2009 dataset at page level, reaching 4.54% of CER. We provide all source code and pre-trained model weights at https://github.com/FactoDeepLearning/DAN.

Essay on "DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition"

The paper presents "DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition," a novel architecture designed to tackle the challenges of handwritten document recognition without the need for text line segmentation. This research addresses the complexities associated with existing methods that require manual segmentation of handwritten documents into lines, which are then recognized independently. The proposed approach adopts an end-to-end model that simultaneously manages text recognition and logical layout identification.

Technical Overview

The key innovation introduced is the Document Attention Network (DAN), which leverages a fully convolutional network (FCN) for feature extraction, coupled with a stack of transformer decoder layers for sequential token prediction. Unlike traditional methods that rely on segmentation, DAN operates directly on whole document images to predict sequences of characters alongside logical layout tokens. This is achieved through a structured output that mimics an XML-like format, where layout tags are utilized to denote the structure and reading order of the document.

DAN capitalizes on attention mechanisms, enabling it to model dependencies across the input document comprehensively. By incorporating both 2D positional encodings in the encoder and 1D positional encodings in the decoder, the architectural design maintains spatial awareness throughout the document analysis process. This configuration assists the transformer decoder in accurately predicting character sequences while considering textual hierarchy defined by layout tags.

Empirical Results

The approach was evaluated on the READ 2016 and RIMES 2009 datasets, with strong results underscoring its effectiveness. Notably, DAN achieved a Character Error Rate (CER) of 3.43% on single-page versions and 3.70% on double-page versions of the READ 2016 dataset. These figures were competitive when compared against the state-of-the-art, which rely on segmented entities and line-specific pre-processing steps. Correspondingly, the model demonstrated exceptional performance on the RIMES 2009 page-level dataset with a CER of 4.54%.

The work also introduced metrics tailored to evaluate comprehensive document recognition capabilities, including the Layout Ordering Error Rate (LOER) and $\mathrm{mAP}_\mathrm{CER}$, measuring layout recognition quality and text-layout association, respectively. The results of these metrics highlight DAN's proficiency in jointly recognizing text and logical structure, a significant advancement in handwriting recognition.

Theoretical Implications and Future Directions

The implications of DAN are profound both practically and theoretically. Practically, it simplifies preprocessing pipelines significantly by eliminating the need for explicit segmentation labels, reducing associated costs and labor. Theoretically, this research contributes to the broader field of document understanding by demonstrating that complex reading orders and structured documents can be interpreted through sequence-to-sequence learning models augmented with attention mechanisms.

Future research may focus on enhancing DAN's adaptability to highly variable and heterogeneous layouts more typical in real-world documents. Further exploration into optimizing prediction speed, especially given the autoregressive nature of transformers, could facilitate more practical applications. Additionally, extending the model to integrate semantic understanding or named entity recognition could pave the way for comprehensive document analysis solutions.

In conclusion, "DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition" presents a sophisticated alternative to traditional segmented handwriting recognition methods. Through its segmentation-free, end-to-end architecture, it efficiently processes entire documents, offering remarkable accuracy and opening new avenues for research and application in document analysis and understanding.